Chapter 7 Functional differences

load("data/data.Rdata")

7.1 Data preparation

# Aggregate bundle-level GIFTs into the compound level
GIFTs_elements <- to.elements(genome_gifts, GIFT_db2)
GIFTs_elements_filtered <- GIFTs_elements[rownames(GIFTs_elements) %in% genome_counts$genome, ]
GIFTs_elements_filtered <- as.data.frame(GIFTs_elements_filtered) %>%
  select_if(~ !is.numeric(.) || sum(.) != 0)

elements <- GIFTs_elements_filtered %>%
  as.data.frame()

# Aggregate element-level GIFTs into the function level
GIFTs_functions <- to.functions(GIFTs_elements_filtered, GIFT_db2)
functions <- GIFTs_functions %>%
  as.data.frame()

# Aggregate function-level GIFTs into overall Biosynthesis, Degradation and Structural GIFTs
GIFTs_domains <- to.domains(GIFTs_functions, GIFT_db2)
domains <- GIFTs_domains %>%
  as.data.frame()

# Get community-weighed average GIFTs per sample
GIFTs_elements_community <- to.community(GIFTs_elements_filtered, genome_counts_filt %>% column_to_rownames(., "genome") %>% tss(), GIFT_db2)
GIFTs_functions_community <- to.community(GIFTs_functions, genome_counts_filt %>% column_to_rownames(., "genome") %>% tss(), GIFT_db2)
GIFTs_domains_community <- to.community(GIFTs_domains, genome_counts_filt %>% column_to_rownames(., "genome") %>% tss(), GIFT_db2)

uniqueGIFT_db<- unique(GIFT_db2[c(2,4,5,6)]) %>% unite("Function",Function:Element, sep= "_", remove=FALSE)

7.2 Genomes GIFT profiles

GIFTs_elements %>%
  as_tibble(., rownames = "MAG") %>%
  reshape2::melt() %>%
  rename(Code_element = variable, GIFT = value) %>%
  inner_join(GIFT_db2,by="Code_element") %>%
  ggplot(., aes(x=Code_element, y=MAG, fill=GIFT, group=Code_function))+
    geom_tile()+
    scale_y_discrete(guide = guide_axis(check.overlap = TRUE))+
    scale_x_discrete(guide = guide_axis(check.overlap = TRUE))+
    scale_fill_gradientn(colours=rev(c("#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#ddf1da")))+
    facet_grid(. ~ Code_function, scales = "free", space = "free")+
    theme_grey(base_size=8)+
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),strip.text.x = element_text(angle = 90))

7.3 Function level

GIFTs_functions_community %>%
    as.data.frame() %>%
    rownames_to_column(var="sample") %>%
    filter(sample!="AD69") %>%
    pivot_longer(!sample,names_to="trait",values_to="gift") %>%
    left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
    ggplot(aes(x=trait,y=time_point,fill=gift)) +
        geom_tile(colour="white", size=0.2)+
        scale_fill_gradientn(colours=rev(c("#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#ddf1da")))+
        facet_grid(type ~ ., scales="free",space="free")

7.4 Element level

GIFTs_elements_community_merged<-GIFTs_elements_community %>%
    as.data.frame() %>%
    rownames_to_column(var="sample") %>%
    filter(sample!="AD69") %>%
    pivot_longer(!sample,names_to="trait",values_to="gift") %>%
    left_join(sample_metadata, by = join_by(sample == Tube_code))%>%
    mutate(functionid = substr(trait, 1, 3)) %>%
    mutate(trait = case_when(
      trait %in% GIFT_db2$Code_element ~ GIFT_db2$Element[match(trait, GIFT_db2$Code_element)],
      TRUE ~ trait
    )) %>%
    mutate(functionid = case_when(
      functionid %in% GIFT_db2$Code_function ~ GIFT_db2$Function[match(functionid, GIFT_db2$Code_function)],
      TRUE ~ functionid
    )) %>%
    mutate(trait=factor(trait,levels=unique(GIFT_db2$Element))) %>%
    mutate(functionid=factor(functionid,levels=unique(GIFT_db2$Function)))

# Create an interaction variable for time_point and sample
GIFTs_elements_community_merged$interaction_var <- interaction(GIFTs_elements_community_merged$sample, GIFTs_elements_community_merged$time_point)
  
ggplot(GIFTs_elements_community_merged,aes(x=interaction_var,y=trait,fill=gift)) +
        geom_tile(colour="white", linewidth=0.2)+
        scale_fill_gradientn(colours=rev(c("#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#ddf1da")))+
        facet_grid(functionid ~ type, scales="free",space="free") +
        theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, size=5),
              strip.text.y = element_text(angle = 0)) + 
        labs(y="Traits",x="Time_point",fill="GIFT")+
  scale_x_discrete(labels = function(x) gsub(".*\\.", "", x))

7.5 Comparison of samples from the 0 Time_point (0_Wild)

7.5.1 GIFTs Functional community

GIFTs_functions_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="0_Wild") %>%
  group_by(Population) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 2 × 3
  Population   MCI     sd
  <chr>      <dbl>  <dbl>
1 Cold_wet   0.346 0.0194
2 Hot_dry    0.327 0.0244
MCI_func_wild <- GIFTs_functions_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="0_Wild")

shapiro.test(MCI_func_wild$value) #normality test

    Shapiro-Wilk normality test

data:  MCI_func_wild$value
W = 0.96385, p-value = 0.4503
res.aov<-aov(value ~ Population, data=MCI_func_wild) #anova test
summary(res.aov)
            Df   Sum Sq   Mean Sq F value Pr(>F)  
Population   1 0.002244 0.0022440   5.025 0.0341 *
Residuals   25 0.011165 0.0004466                 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

7.5.1.1 GIFT test visualisation

GIFTs_functions_community %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="0_Wild") %>%
  select(c(1:21, 24)) %>%
  pivot_longer(-c(sample,Population),names_to = "trait", values_to = "value") %>%
  mutate(trait = case_when(
      trait %in% GIFT_db2$Code_function ~ GIFT_db2$Function[match(trait, GIFT_db2$Code_function)],
      TRUE ~ trait
    )) %>%
  mutate(trait=factor(trait,levels=unique(GIFT_db2$Function))) %>%
  ggplot(aes(x=value, y=Population, group=Population, fill=Population, color=Population)) +
    geom_boxplot() +
    scale_color_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#008080', "#d57d2c")) +
      scale_fill_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#00808050', "#d57d2c50")) +
    facet_grid(trait ~ ., space="free", scales="free") +
              theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
              strip.text.y = element_text(angle = 0)) + 
        labs(y="Traits",x="Metabolic capacity index")

7.5.2 GIFTs Domain community

GIFTs_domains_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="0_Wild") %>%
  group_by(species) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 2 × 3
  species             MCI     sd
  <chr>             <dbl>  <dbl>
1 Podarcis_liolepis 0.337 0.0200
2 Podarcis_muralis  0.350 0.0232
MCI_domain_wild <- GIFTs_domains_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="0_Wild")

shapiro.test(MCI_domain_wild$value) #normality test

    Shapiro-Wilk normality test

data:  MCI_domain_wild$value
W = 0.97775, p-value = 0.8085
res.aov<-aov(value ~ Population, data=MCI_domain_wild) #anova test
summary(res.aov)
            Df   Sum Sq   Mean Sq F value Pr(>F)
Population   1 0.000977 0.0009768    1.98  0.172
Residuals   25 0.012336 0.0004935               

7.5.3 GIFTs Elements community

GIFTs_elements_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="0_Wild") %>%
  group_by(species) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 2 × 3
  species             MCI     sd
  <chr>             <dbl>  <dbl>
1 Podarcis_liolepis 0.313 0.0329
2 Podarcis_muralis  0.345 0.0233
MCI_element_wild <- GIFTs_elements_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="0_Wild")

shapiro.test(MCI_element_wild$value) #normality test

    Shapiro-Wilk normality test

data:  MCI_element_wild$value
W = 0.96004, p-value = 0.3703
res.aov<-aov(value ~ Population, data=MCI_element_wild) #anova test
summary(res.aov)
            Df   Sum Sq  Mean Sq F value  Pr(>F)   
Population   1 0.006235 0.006235   8.709 0.00679 **
Residuals   25 0.017898 0.000716                   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

7.6 Comparison of samples from the 1st Time_point (1_Acclimation)

7.6.1 GIFTs Functional community

GIFTs_functions_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="1_Acclimation") %>%
  group_by(species) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 2 × 3
  species             MCI     sd
  <chr>             <dbl>  <dbl>
1 Podarcis_liolepis 0.348 0.0158
2 Podarcis_muralis  0.329 0.0319
MCI_func_accli <- GIFTs_functions_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="1_Acclimation")

shapiro.test(MCI_func_accli$value) #normality test

    Shapiro-Wilk normality test

data:  MCI_func_accli$value
W = 0.92832, p-value = 0.0707
res.aov<-aov(value ~ Population, data=MCI_func_accli) #anova test
summary(res.aov)
            Df   Sum Sq   Mean Sq F value Pr(>F)
Population   1 0.002028 0.0020275   2.667  0.116
Residuals   24 0.018247 0.0007603               

7.6.1.1 GIFT test visualisation

GIFTs_functions_community %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="1_Acclimation") %>%
  select(c(1:21, 24)) %>%
  pivot_longer(-c(sample,Population),names_to = "trait", values_to = "value") %>%
  mutate(trait = case_when(
      trait %in% GIFT_db2$Code_function ~ GIFT_db2$Function[match(trait, GIFT_db2$Code_function)],
      TRUE ~ trait
    )) %>%
  mutate(trait=factor(trait,levels=unique(GIFT_db2$Function))) %>%
  ggplot(aes(x=value, y=Population, group=Population, fill=Population, color=Population)) +
    geom_boxplot() +
    scale_color_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#008080', "#d57d2c")) +
      scale_fill_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#00808050', "#d57d2c50")) +
    facet_grid(trait ~ ., space="free", scales="free") +
              theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
              strip.text.y = element_text(angle = 0)) + 
        labs(y="Traits",x="Metabolic capacity index")

7.6.2 GIFTs Domain community

GIFTs_domains_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="1_Acclimation") %>%
  group_by(species) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 2 × 3
  species             MCI     sd
  <chr>             <dbl>  <dbl>
1 Podarcis_liolepis 0.344 0.0133
2 Podarcis_muralis  0.328 0.0371
MCI_domain_accli <- GIFTs_domains_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="1_Acclimation")

shapiro.test(MCI_domain_accli$value) #normality test

    Shapiro-Wilk normality test

data:  MCI_domain_accli$value
W = 0.95031, p-value = 0.2358
res.aov<-aov(value ~ Population, data=MCI_domain_accli) #anova test
summary(res.aov)
            Df   Sum Sq   Mean Sq F value Pr(>F)
Population   1 0.001423 0.0014229   1.459  0.239
Residuals   24 0.023404 0.0009751               

7.6.3 GIFTs Elements community

GIFTs_elements_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="1_Acclimation") %>%
  group_by(species) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 2 × 3
  species             MCI     sd
  <chr>             <dbl>  <dbl>
1 Podarcis_liolepis 0.350 0.0225
2 Podarcis_muralis  0.331 0.0319
MCI_element_accli <- GIFTs_elements_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="1_Acclimation")

shapiro.test(MCI_element_accli$value) #normality test

    Shapiro-Wilk normality test

data:  MCI_element_accli$value
W = 0.91889, p-value = 0.04235
res.aov<-aov(value ~ Population, data=MCI_element_accli) #anova test
summary(res.aov)
            Df   Sum Sq   Mean Sq F value Pr(>F)
Population   1 0.002272 0.0022722   2.685  0.114
Residuals   24 0.020309 0.0008462               

7.7 Comparison of samples from the 5th Time_point (5_Post-FMT1)

7.7.1 GIFTs Functional community

GIFTs_functions_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="5_Post-FMT1") %>%
  group_by(type) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 3 × 3
  type          MCI     sd
  <chr>       <dbl>  <dbl>
1 Control     0.373 0.0247
2 Hot_control 0.372 0.0367
3 Treatment   0.353 0.0186
MCI_tm5 <- GIFTs_functions_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="5_Post-FMT1")

shapiro.test(MCI_tm5$value)#normality test

    Shapiro-Wilk normality test

data:  MCI_tm5$value
W = 0.92343, p-value = 0.05415
res.aov<-aov(value ~ type, data=MCI_tm5)#anova test
summary(res.aov)
            Df  Sum Sq   Mean Sq F value Pr(>F)
type         2 0.00216 0.0010798   1.371  0.274
Residuals   23 0.01811 0.0007875               

7.7.1.1 GIFT test visualisation

GIFTs_functions_community %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="5_Post-FMT1") %>%
  select(c(1:21, 27)) %>%
  pivot_longer(-c(sample,type),names_to = "trait", values_to = "value") %>%
  mutate(trait = case_when(
      trait %in% GIFT_db2$Code_function ~ GIFT_db2$Function[match(trait, GIFT_db2$Code_function)],
      TRUE ~ trait
    )) %>%
  mutate(trait=factor(trait,levels=unique(GIFT_db2$Function))) %>%
  ggplot(aes(x=value, y=type, group=type, fill=type, color=type)) +
    geom_boxplot() +
    scale_color_manual(name="type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA","#d57d2c","#76b183")) +
      scale_fill_manual(name="type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA50","#d57d2c50","#76b18350")) +
    facet_grid(trait ~ ., space="free", scales="free") +
              theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
              strip.text.y = element_text(angle = 0)) + 
        labs(y="Traits",x="Metabolic capacity index")

7.7.2 GIFTs Domain community

GIFTs_domains_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="5_Post-FMT1") %>%
  group_by(type) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 3 × 3
  type          MCI     sd
  <chr>       <dbl>  <dbl>
1 Control     0.362 0.0223
2 Hot_control 0.362 0.0353
3 Treatment   0.341 0.0232
MCI_tm5_domain <- GIFTs_domains_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="5_Post-FMT1")

shapiro.test(MCI_tm5_domain$value)#normality test

    Shapiro-Wilk normality test

data:  MCI_tm5_domain$value
W = 0.91677, p-value = 0.03779
res.aov<-aov(value ~ type, data=MCI_tm5_domain)#anova test
summary(res.aov)
            Df   Sum Sq   Mean Sq F value Pr(>F)
type         2 0.002492 0.0012461   1.615  0.221
Residuals   23 0.017746 0.0007715               

7.7.3 GIFTs Elements community

GIFTs_elements_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="5_Post-FMT1") %>%
  group_by(type) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 3 × 3
  type          MCI     sd
  <chr>       <dbl>  <dbl>
1 Control     0.380 0.0280
2 Hot_control 0.379 0.0372
3 Treatment   0.359 0.0214
MCI_tm5_ele<- GIFTs_elements_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="5_Post-FMT1")

shapiro.test(MCI_tm5_ele$value)#normality test

    Shapiro-Wilk normality test

data:  MCI_tm5_ele$value
W = 0.97214, p-value = 0.6794
res.aov<-aov(value ~ type, data=MCI_tm5_ele)#anova test
summary(res.aov)
            Df   Sum Sq   Mean Sq F value Pr(>F)
type         2 0.002312 0.0011561   1.297  0.293
Residuals   23 0.020506 0.0008916               

7.8 Comparison of samples from the 6th Time_point (6_Post-FMT2)

7.8.1 GIFTs Functional community

GIFTs_functions_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="6_Post-FMT2") %>%
  group_by(type) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 3 × 3
  type          MCI     sd
  <chr>       <dbl>  <dbl>
1 Control     0.352 0.0223
2 Hot_control 0.350 0.0293
3 Treatment   0.346 0.0255
MCI_tm6 <- GIFTs_functions_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="6_Post-FMT2")

shapiro.test(MCI_tm6$value)#normality test

    Shapiro-Wilk normality test

data:  MCI_tm6$value
W = 0.96248, p-value = 0.4203
res.aov<-aov(value ~ type, data=MCI_tm6)#anova test
summary(res.aov)
            Df  Sum Sq   Mean Sq F value Pr(>F)
type         2 0.00017 0.0000852   0.127  0.881
Residuals   24 0.01604 0.0006682               

7.8.1.1 GIFT test visualisation

GIFTs_functions_community %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="6_Post-FMT2") %>%
  select(c(1:21, 27)) %>%
  pivot_longer(-c(sample,type),names_to = "trait", values_to = "value") %>%
  mutate(trait = case_when(
      trait %in% GIFT_db2$Code_function ~ GIFT_db2$Function[match(trait, GIFT_db2$Code_function)],
      TRUE ~ trait
    )) %>%
  mutate(trait=factor(trait,levels=unique(GIFT_db2$Function))) %>%
  ggplot(aes(x=value, y=type, group=type, fill=type, color=type)) +
    geom_boxplot() +
    scale_color_manual(name="type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA","#d57d2c","#76b183")) +
      scale_fill_manual(name="type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA50","#d57d2c50","#76b18350")) +
    facet_grid(trait ~ ., space="free", scales="free") +
              theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
              strip.text.y = element_text(angle = 0)) + 
        labs(y="Traits",x="Metabolic capacity index")

7.8.2 GIFTs Domain community

GIFTs_domains_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="6_Post-FMT2") %>%
  group_by(type) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 3 × 3
  type          MCI     sd
  <chr>       <dbl>  <dbl>
1 Control     0.348 0.0279
2 Hot_control 0.352 0.0322
3 Treatment   0.340 0.0243
MCI_domains_tm6 <- GIFTs_domains_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="6_Post-FMT2")

shapiro.test(MCI_domains_tm6$value)#normality test

    Shapiro-Wilk normality test

data:  MCI_domains_tm6$value
W = 0.94369, p-value = 0.1502
res.aov<-aov(value ~ type, data=MCI_domains_tm6)#anova test
summary(res.aov)
            Df  Sum Sq   Mean Sq F value Pr(>F)
type         2 0.00074 0.0003699   0.461  0.636
Residuals   24 0.01925 0.0008021               

7.8.3 GIFTs Elements community

GIFTs_elements_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="6_Post-FMT2") %>%
  group_by(type) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 3 × 3
  type          MCI     sd
  <chr>       <dbl>  <dbl>
1 Control     0.357 0.0215
2 Hot_control 0.347 0.0302
3 Treatment   0.350 0.0293
MCI_ele_tm6 <- GIFTs_elements_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="6_Post-FMT2")

shapiro.test(MCI_ele_tm6$value) #normality test

    Shapiro-Wilk normality test

data:  MCI_ele_tm6$value
W = 0.95139, p-value = 0.2316
res.aov<-aov(value ~ type, data=MCI_ele_tm6) #anova test
summary(res.aov)
            Df   Sum Sq   Mean Sq F value Pr(>F)
type         2 0.000488 0.0002438   0.328  0.724
Residuals   24 0.017851 0.0007438               

7.9 Domain level

7.9.1 Comparison of samples from the 0 Time_point (0_Wild)

#Merge the functional domains with the metadata
merge_gift_wild<- GIFTs_domains_community %>% 
  as.data.frame() %>% 
  rownames_to_column(., "Tube_code") %>% 
  inner_join(., sample_metadata_wild, by="Tube_code")
#Biosynthesis
p1 <-merge_gift_wild %>%
  ggplot(aes(x=species,y=Biosynthesis,color=species,fill=species))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 0.5, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "Species")

#Degradation
p2 <-merge_gift_wild %>%
  ggplot(aes(x=species,y=Degradation,color=species,fill=species))+
  geom_jitter(width = 0.2, size = 1.45, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 0.5, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "Species")

#Structure
p3 <-merge_gift_wild %>%
  ggplot(aes(x=species,y=Structure,color=species,fill=species))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 3, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "Species")

7.9.2 Comparison of samples from the 1st Time_point (1_Acclimation)

#Merge the functional domains with the metadata
merge_gift_accli<- GIFTs_domains_community %>% 
  as.data.frame() %>% 
  rownames_to_column(., "Tube_code") %>% 
  inner_join(., sample_metadata_accli, by="Tube_code")
#Biosynthesis
p1 <-merge_gift_accli %>%
  ggplot(aes(x=Population,y=Biosynthesis,color=Population,fill=Population))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 0.5, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "Population")

#Degradation
p2 <-merge_gift_accli %>%
  ggplot(aes(x=Population,y=Degradation,color=Population,fill=Population))+
  geom_jitter(width = 0.2, size = 1.45, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 0.5, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "Population")

#Structure
p3 <-merge_gift_accli %>%
  ggplot(aes(x=Population,y=Structure,color=Population,fill=Population))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 3, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "Population")

7.9.3 Comparison of samples from the 5th Time_point (5_Post-FMT1)

#Merge the functional domains with the metadata
merge_gift_tm5<- GIFTs_domains_community %>% 
  as.data.frame() %>% 
  rownames_to_column(., "Tube_code") %>% 
  inner_join(., sample_metadata_tm5, by="Tube_code")
#Biosynthesis
p1 <-merge_gift_tm5 %>%
  ggplot(aes(x=type,y=Biosynthesis,color=type,fill=type))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 0.5, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "Type")

#Degradation
p2 <-merge_gift_tm5 %>%
  ggplot(aes(x=type,y=Degradation,color=type,fill=type))+
  geom_jitter(width = 0.2, size = 1.45, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 0.5, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "Type")

#Structure
p3 <-merge_gift_tm5 %>%
  ggplot(aes(x=type,y=Structure,color=type,fill=type))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 3, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "Type")

7.9.4 Comparison of samples from the 6th Time_point (6_Post-FMT2)

7.9.4.1 CI vs CC

#Merge the functional domains with the metadata
merge_gift_TM6 <- GIFTs_domains_community %>% 
  as.data.frame() %>% 
  rownames_to_column(., "Tube_code") %>% 
  inner_join(., sample_metadata_TM6, by="Tube_code")
#Biosynthesis
p1 <-merge_gift_TM6 %>%
  ggplot(aes(x=type,y=Biosynthesis,color=type,fill=type))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 0.5, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "Type")

#Degradation
p2 <-merge_gift_TM6 %>%
  ggplot(aes(x=type,y=Degradation,color=type,fill=type))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 0.5, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "type")

#Structure
p3 <-merge_gift_TM6 %>%
  ggplot(aes(x=type,y=Structure,color=type,fill=type))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 3, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "type")

7.9.4.2 CI vs WC

#Merge the functional domains with the metadata
merge_gift_TM8 <- GIFTs_domains_community %>% 
  as.data.frame() %>% 
  rownames_to_column(., "Tube_code") %>% 
  inner_join(., sample_metadata_TM8, by="Tube_code")
#Biosynthesis
p1 <-merge_gift_TM8 %>%
  ggplot(aes(x=type,y=Biosynthesis,color=type,fill=type))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 0.5, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "Type")

#Degradation
p2 <-merge_gift_TM8 %>%
  ggplot(aes(x=type,y=Degradation,color=type,fill=type))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 0.5, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "type")

#Structure
p3 <-merge_gift_TM8 %>%
  ggplot(aes(x=type,y=Structure,color=type,fill=type))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 3, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "type")